WiFi systems face a variety of unique challenges. For example multiple factors effect throughput of a WiFi system, including but not limited to: complex interference in the Industrial, Scientific and Medical (ISM) spectrum, poor spectral efficiency of 802.11 media access control protocol (MAC), and starvation problems associated with hidden nodes and exposed nodes.
If we imagine the public WiFi system as a complex and giant system, the system will produce some “performance outputs” (such as the overall system capacity, the overall system coverage, etc.) based on some “control inputs.” For example, control inputs can include the operating frequency of various access points, instantaneous beam index, the instantaneous transmit data rate, transmit MIMO mode (spatial mux or spatial div), the instantaneous transmit power, and the carrier sensing threshold. Such a system will also exhibit some “observations” including for example, traffic load data, transmission data, and ACK/NAK flows.
Conventional WiFi systems are similar to an “open loop” system in the sense that the control inputs (such as the operating channel, beam, transmit power, carrier sensing thresholds) are pre-configured and remain static. However, the dynamics of the WiFi environment are time varying and random. For example, randomness is driven by channel fading, bursty traffic arrival, and random interference. As a result, the existing “open-loop” approach (static approach) for running conventional WiFi systems is sub-optimal as the “control inputs” fail to exploit the random and time varying “system states” of the WiFi system.
In addition, there are various important parameters in the 802.11 MAC that could be optimized so as to enhance the spectral efficiency of the 802.11 MAC layer. They are namely the transmit rate, the transmit power as well as the carrier sense threshold. However, existing techniques regarding rate adaptation are based only on heuristic approaches. Since the rate adaptation loop in previous approaches is driven by ACK/NAK feedback, it suffers from the disadvantage of slow response. There is no power control implemented or carrier sensing threshold adaptation in most of the WiFi systems available today. Furthermore, existing techniques fail to exploit the unique features of the MIMO physical layer.
Unlike the resource optimization problems in cellular networks that operate in the licensed spectrum, the optimization problem for WiFi systems is very tricky and challenging due to the distributive implementation of 802.11. The IEEE 802.11 MAC protocol uses the CSMA (listen-before-talk) mechanism to resolve potential conflicts in channel access. To allow for robust operation, in fact, it has been shown that the power of the advanced MIMO technology in 802.11n WiFi systems cannot be fully unleashed without proper optimization at the MAC layer.
The above-described deficiencies of conventional WiFi optimization network techniques are merely intended to provide an overview of some of problems of current technology, and are not intended to be exhaustive. Other problems with the state of the art, and corresponding benefits of some of the various non-limiting embodiments described herein, may become further apparent upon review of the following detailed description.